Today, mechanization has progressed in a variety of fields due to technological advancements. When an object is operated using a robot, there is a need to identify the position, type, and other attributes of the object; therefore, an image obtained by a CCD camera or other means is processed and analyzed, thereby fulfilling a function corresponding to the human eye.
A variety of types of objects exist in the everyday environment of human lifestyle, from rigid bodies that have shapes that do not change at all to objects such as cloth or paper that change to a variety of shapes. When performing automation using a robot, there is a need to identify what the object is from an image irrespective of the shape of the object.
In recent years, SIFT (non-patent document 1) is commonly used for identification of objects by image processing. This method enables image processing even when the manner in which the object is visible changes to a certain degree, but is image processing that basically assumes that the object is a rigid body, and is therefore difficult to apply for identification of, e.g., (1) soft items such as clothing that may assume a variety of shapes through folded overlapping, bending, creasing, or the like, (2) asphalt portions of roads and bare earth or grass portions on the shoulder, (3) foreign objects that are mixed in or layered onto the subject, such as dust on a floor, or (4) vegetables and fruits, which assume different outer shapes even between the same type due to, e.g., bending or the shape of leaves. Therefore, in order to provide lifestyle assistance in the everyday environment of humans, there is a need for, e.g., (1) an image processing method for appropriately identifying soft items, in light of cases in which automated machines such as robots handle laundry, which is a soft item; (2) an image processing method for an automated travel system in which electric wheelchairs or automobiles used by the visually impaired travel along designated positions; and (3) an image processing method for identifying soiled portions on the floor when an automatic vacuum cleaner cleans the floor. In addition, there is also a need for (4) an image processing method for accurately classifying and/or identifying objects that may assume a variety of outer shapes in industry settings such as classification of vegetables and fruits in a food factory.
With regards to image processing methods in relation to soft items, a variety of image features have been conventionally used. For example, Kakikura et al. realized an isolation task using color information (see non-patent document 2). Ono et al. proposed a method for expressing, with regards to a square cloth product such as a handkerchief, a state in which a part of the product is folded (see non-patent document 3). Kita et al. proposed a method for using a three-dimensional variable shape model, and applying the model to a group of three-dimensional points obtained by measurement (see non-patent document 4). However, in these existing studies, the type of cloth product is provided in advance, or identification information for specifying the cloth product is defined as, e.g., the color of the material, and information for specifying the cloth product or the like is necessary in advance.
If there is a method making it possible to extract feature descriptions of a cloth product or the like from an image that can be generically used without requiring information for specifying the product as described above, it is possible to classify a plurality of types of products into identical products for lifestyle support or in a cleaning plant. In addition, if there is a method making it possible to identify the classified product, the method might be useful for automation using a robot or the like. Accordingly, Osawa et al. (see non-patent document 5), and Abbeel et al. (see
non-patent document 6) propose methods for identifying the outline or the position of the bottom end point of a cloth product while a robot is operating the cloth product, and identifying the type of the cloth product.